Modern software systems are often realized by coordinating multiple heterogeneous parts, each responsible for specific tasks. These parts must work together seamlessly to satisfy the overall system requirements. To verify such complex systems, we have developed a non-intrusive coordination frame-work capable of performing formal analysis of heterogeneous parts that exchange data and include real-time capabilities. The framework utilizes a linguistic extension-which is implemented as a central broker and a domain-specific language-for the integration of heterogeneous languages and coordination of parts. Moreover, abstract rule templates are reified as language adapters for non-intrusive communications with the broker. The framework is implemented using rewriting logic (Maude), and its applicability is demonstrated by verifying certain correctness properties of a heterogeneous road-rail crossing system.

The Business Process Modeling Notation (BPMN) is a widely used standard notation for defining intra- and inter-organizational workflows. However, the informal description of the BPMN execution semantics leads to different interpretations of BPMN elements and difficulties in checking behavioral properties. In this article, we propose a formalization of the execution semantics of BPMN that, compared to existing approaches, covers more BPMN elements while also facilitating property checking. Our approach is based on a higher-order transformation from BPMN models to graph transformation systems. To show the capabilities of our approach, we implemented it as an open-source web-based tool.

Workflow analysis usually requires domain-specific knowledge from the domain experts, making it a relatively manual process. In addition, workflows often cross organisational boundaries. As a result, minor local modifications in the workflow of a collaborative partner may be propagated to other concurrently running tasks of the workflow, which is difficult for the domain experts to recognise since they only have a limited (local) view of the workflow. Therefore, changes in cross-organisational workflows may result in significant adverse impacts. This paper presents a resource-sensitive formal modelling language, , which has explicit notions of task dependencies, qualitative assessment of resources, time advancement and method execution deadlines. The language allows the workflow analysers to estimate the effect of changes in collaborative workflows with respect to cost in terms of execution time. This paper proposes a static analysis to compute the worst execution time of a cross-organisational workflow modelled in by defining a compositional function that translates an program to a set of cost equations.

Debugging is an essential part of software maintenance and evolution since it allows software developers to analyze program execution step by step. Understanding a program is required to fix potential flaws, alleviate bottlenecks, and implement new desired features. Thus, software developers spend a large percentage of their time validating and debugging software, resulting in high software maintenance and evolution cost. We aim to reduce this cost by providing a novel visual debugging tool to software developers to foster program comprehension during debugging. Our debugging tool visualizes program execution information graphically as an object diagram and is fully integrated into the popular Java development environment IntelliJ IDEA. Moreover, the object diagram allows interactions to explore program execution information in more detail. A demonstration of our tool is available at https://www.youtube.com/watch?v=lU_OgotweRk.

Today, in many real-world applications of machine learning algorithms, the data is stored on multiple sources instead of at one central repository. In many such scenarios, due to privacy concerns and legal obligations, e.g., for medical data, and communication/computation overhead, for instance for large scale data, the raw data cannot be transferred to a center for analysis. Therefore, new machine learning approaches are proposed for learning from the distributed data in such settings. In this paper, we extend the distributed Extremely Randomized Trees (ERT) approach w.r.t. privacy and scalability. First, we extend distributed ERT to be resilient w.r.t. the number of colluding parties in a scalable fashion. Then, we extend the distributed ERT to improve its scalability without any major loss in classification performance. We refer to our proposed approach as k-PPD-ERT or Privacy-Preserving Distributed Extremely Randomized Trees with k colluding parties.

Abstract Model management is a central activity in Software Engineering. The most challenging aspect of model management is to keep inter-related models consistent with each other while they evolve. As a consequence, there is a lot of scientific activity in this area, which has produced an extensive body of knowledge, methods, results and tools. The majority of these approaches, however, are limited to binary inter-model relations; i.e. the synchronisation of exactly two models. Yet, not every multi-ary relation can be factored into a family of binary relations. In this paper, we propose and investigate a novel comprehensive system construction, which is able to represent multi-ary relations among multiple models in an integrated manner and thus serves as a formal foundation for artefacts used in consistency management activities involving multiple models. The construction is based on the definition of partial commonalities among a set of models using the same language, which is used to denote the (local) models. The main theoretical results of this paper are proofs of the facts that comprehensive systems are an admissible environment for (i) applying formal means of consistency verification (diagrammatic predicate framework), (ii) performing algebraic graph transformation (weak adhesive HLR category), and (iii) that they generalise the underlying setting of graph diagrams and triple graph grammars.

Background: Internet-Delivered Psychological Treatment (IDPT) systems have the potential to provide evidence-based mental health treatments for a far-reaching population at a lower cost. However, most of the current IDPT systems follow a tunnel-based treatment process and do not adapt to the needs of different patients'. In this paper, we explore the possibility of applying Natural Language Processing (NLP) for personalizing mental health interventions. Objective: The primary objective of this study is to present an adaptive strategy based on NLP techniques that analyses patient-authored text data and extract depression symptoms based on a clinically established assessment questionnaire, PHQ-9. Method: We propose a novel word-embedding (Depression2Vec) to extract depression symptoms from patient authored text data and compare it with three state-of-the-art NLP techniques. We also present an adaptive IDPT system that personalizes treatments for mental health patients based on the proposed depression symptoms detection technique. Result: Our results indicate that the performance of proposed embedding Depression2Vec is comparable to WordNet, but in some cases, the former outperforms the latter with respect to extracting depression symptoms from the patient-authored text. Conclusion: Although the extraction of symptoms from text is challenging, our proposed method can effectively extract depression symptoms from text data, which can be used to deliver personalized intervention.

Model management is a central activity in Software Engineering. The most challenging aspect of model management is to keep models consistent with each other while they evolve. As a consequence, there has been increasing activity in this area, which has produced a number of approaches to address this synchronization challenge. The majority of these approaches, however, is limited to a binary setting; i.e. the synchronization of exactly two models with each other. A recent Dagstuhl seminar on multidirectional transformations made it clear that there is a need for further investigations in the domain of general multiple model synchronization simply because not every multiary consistency relation can be factored into binary ones. However, with the help of an auxiliary artifact, which provides a global view over all models, multiary synchronization can be achieved by existing binary model synchronization means. In this paper, we propose a novel comprehensive system construction to produce such an artifact using the same underlying base modelling language as the one used to define the models. Our approach is based on the definition of partial commonalities among a set of aligned models. Comprehensive systems can be shown to generalize the underlying categories of graph diagrams and triple graph grammars and can efficiently be implemented in existing tools.

Abstract: Distributed systems modelling often involves a set of heterogeneous models where each model specifies a set of local constraints capturing a specific view of the system. In real life, distributed systems are often loosely connected and interdependencies are not defined into their software model. This limits the scope of optimization of distributed resources. In this paper, we merge heterogeneous models of distributed systems and articulate distributed resource constraints via inter-metamodel constraints. We apply model-driven engineering and use model transformation rules to construct an epistemic game theory model for the purpose of optimizing distributed resource allocation. Since the application of transformation rules normally do not guarantee the satisfaction of constraints when applied on a model, it requires a conformance checking which is an expensive operation. To overcome this problem, we introduce the concept of compliant rule and coordinate with other rules for efficient m (More) \nPublished with permission from SciTePress. Copyright 2017 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

Metamodelling and model transformation play important roles in model-driven engineering as they can be used to define domain-specific modelling languages. During the modelling phase, modellers encode domain knowledge into models which may include both structural and behavioral aspects of a system. The contribution of this paper is a new web-based metamodelling and model transformation tool called WebDPF based on the Diagram Predicate Framework (DPF). WebDPF supports multilevel diagrammatic metamodelling and specification of model constraints, and it supports diagrammatic development and analysis of model transformation systems. We show how the support for model transformation systems in WebDPF can be exploited to (i) support auto-completion of partial models thereby enhancing modelling efficiency, and (ii) provide execution semantics for workflow models. Furthermore, we illustrate how WebDPF incorporates a scalable model navigation facility designed to enable users to inspect and query large models.

Abstract Recommender technologies have been developed to give helpful predictions for decision making under uncertainty. An extensive amount of research has been done to increase the quality of such predictions, currently the methods based on matrix factorization are recognized as one of the most efficient. The focus of this paper is to extend a matrix factorization algorithm with content awareness to increase prediction accuracy. A recommender system prototype based on the resulting Extended Content-Boosted Matrix Factorization Algorithm is designed, developed and evaluated. The algorithm has been evaluated by empirical evaluation, which starts with creating of an experimental design, then conducting off-line empirical tests with accuracy measurement. The result revealed further potential of the content awareness in matrix factorization methods, which has not been fully realized in the generalized alignment-biased algorithm by Nguyen and Zhu and uncovers opportunities for future research.

In Model Driven Engineering (MDE) models are the main artefacts of the software development process. Model transformations are used both in the software development phase and for verification and simulation of the system behaviour. Hence, tools and languages for describing model transformations are essential in MDE. While many practical transformation languages and tools have been proposed, there is still the need for formal foundations of model transformations.

In this paper we introduce a (mathematical) model for Dynamic Content Based e-learning systems (DCM). The model is based on 3 types of maps: Knowledge Map, Learning Map and Student Map. Each map corresponds, respectively, to a model of the knowledge space, the learning process and students participating in the learning process. After introducing the model we show how the model can be used in mathematical education on a case from arithmetic. We also discuss how the next version of the DCM system may evolve, based on the given model.

The raise of the abstraction level of programming languages has resulted in the usage of models and model transformations in software development processes. As a consequence of the usage of models as input to model transformation tools, there is a need for formal modeling languages and formal transformation definition techniques which can be employed to automatically translate between (and integrate) models. Therefore, a major focus of our research is on the formalization of modeling and model transformation in the generic formalism, Diagrammatic Predicate Logic (DPL). In this paper, we discuss a formalization approach to model transformation definitions based on DPL. Then, based on this formalization, some features of model transformations such as traceability, bidirectionality and compositionality are discussed.